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Dhaka, Bangladesh’s capital, already has a congestion problem to handle the ever-growing demand for traffic. The usage of private cars cannot be stopped by charging and the town is not encouraged by quality public transport. It is impossible to enforce congestion prices here in line with traditional cordon pricing systems since the region uses unusual land patterns. However, the current project Mass Rapid Transit (MRT) Line 6, which will be built by 2021, provides the prospect of congestion pricing. A price and optimum approach were established for this article. The congestion price is only payable for the segments and is accessible for private cars under this system. Two urban street segments along the MRT route were selected for the study and congestion toll for a private car is estimated for each segment separately. The sum of the toll in monetary terms is determined using certain associated parameters from the discrepancy from the actual Level of Service (LOS) travel time and traffic flow to the desired LOS. The outcome has shown that the price per passenger car is $0.3 - $0.44. The price is flexible, which means it will vary b ased on traffic volume. The findings for politicians to enforce congestion pricing are viewed as recommendations.

Congestion Pricing has been implemented in many developed countries adopting different pricing schemes to impose surcharges for the vehicle users in the road network. This results in discouraging passenger vehicles to drive through a particular road segment to reduce congestion. Over the last few decades, London, Stockholm, Singapore, and several cities implemented this scheme and are still running this process to mitigate congestion [

However, Dhaka’s land-use trend does not lend itself to an especially successful scheme for congestion pricing. While there is ample north-south connectivity, east-west connectivity is lacking. The only viable form of public transit in certain dense urban areas is by bus. Furthermore, buses are not up to the mark or so-called below standard. In this circumstance, a cordon implementation and toll imposing on private cars to allow mass people to utilize public transit will be challenging. The ongoing construction of Mass Rapid Transit Line 6 (metro rail) will be built by the end of 2021 [

Several methods are followed for road pricing such as cordon-based, time-based, distance-based, and congestion-based [

Moreover, several studies have been attempted in Dhaka city in recent years. Bakkarsiddique et al. (2013) demonstrated a survey-based result to investigate the potential response to congestion cost and found that there is a significant portion of automobile users in Dhaka who are alert to the cost of congestions and are prepared to switch to alternate modes [

Li (1999) created a basic model for the best approach to present toll for ALS congestion [

The rest of the paper is organized as follows. Section 2 describes the case review. Section 3 describes the methodology for the pricing scheme and toll estimation. Data collected from the field survey are analyzed and followed by LOS is determined in Section 4. Congestion tolls are also estimated in this section. Section 5 concludes the study by presenting guidelines to the policymakers.

The Electronic Road Pricing (ERP) framework in Singapore was widely credited with the reduction of traffic congestion and time. The Cordon Price Schemes, which was substituted in 1998 by ERP, was formerly introduced according to the Area Licensing Scheme (ALS). The ALS consisted of a restricted region (RZ) in the city covering the whole CBD and Chinatown’s industrial districts and most of the Orchard Road Retail Corridor up to Scotts Road. The initial aim of early ALS was to avoid large usage in early morning peak hours of private cars to alleviate congestion for commuting purposes to the CBD. After more than two decades, the ALS had proved to be quite successful, but some studies have challenged the fairness of the congestion charges. Following this, Li (1999) established a basic model for checking how close the congestion toll of the ALS was to the optimum. Only the overall pay scale of car owners was used based on traffic counts details and facts about the importance of time saves were carried on. The “Equation (1)” developed for congestion toll (CT) estimation was as follows.

CT= [ ( T w , non-ALS − T w , ALS ) δ w ω X nonALS − X ALS + ( T m , non-ALS − T m , ALS ) δ m ω X non-ALS − X ALS ] ] x (1)

where,

T = time; δ, the value of time as a percentage of average wage rate; ω, average wage rate; m, the moving state of driving; w, the waiting state of driving; ALS = the ALS period; non-ALS = the non-ALS period; x = traffic level at any time.

This paper modifies Li’s (1999) equation and uses a technique for the calculation of the congestion fee for the vehicle using Level of Service (LOS) later. Although the price framework established in this analysis is not a cordon pricing methodology, the input parameters of the Li research are the same as the input methodology. By the advancement of this article, the reliability of the toll assessment approach is justified.

This pricing scheme can be named Segment Pricing. For describing the methodology first urban street segment “Mirpur 10 Intersection to Agargaon Intersection” is addressed as segment 1 and the second urban street segment “Agargaon Intersection to Bijoy Sarani Intersection” is addressed as segment 2.

Along with both segments, the morning heading is marked in

From the discussion of section 2, it can be said that equation 1 is very useful to check the congestion toll where it is already imposed. Irrespective of any pricing scheme Equation (1) can be written as

CT= [ ( T w , nonpriced − T w , priced ) δ w ω X nonpriced − X priced + ( T m , nonpriced − T m , priced ) δ m ω X nonpriced − X priced ] x (2)

where,

priced = the period considering congestion pricing is in operation; non-priced = the period considering no congestion pricing. The rest of the parameters remain the same.

However, congestion rates cannot be determined by way of “Equation (2)”, instead, it is helpful to modify the current rates due to the adjustment of travel time and traffic frequency. Now that we have traced back to the LOS definition, we realize that LOS is a qualitative indicator that demonstrates travel efficiency in an urban road section. Better LOS implies improved speed and thus a lower time of drive and therefore a lower time of waiting at the intersection. From field data, one can conveniently find a complete travel time at the moving state and time at the intersections in the current condition. Applying “Urban street segments”, “signalized intersections” of HCM (2016) respectively, average speeds of traffic and waiting time at the intersection that correlates to an ideal LOS (desired LOS better than existing LOS). Existing LOS’ indicates the current conditions of travel time and traffic intensity from field data. “Desired LOS” is the most achievable and awaited LOS. Since LOS A, B can’t be achieved, and the toll price is really high. Alternative routes can therefore be congested. Due to the competitive method forecast, first calculated toll prices of LOS can be altered in a short space of time because of price changes in demand. It is easier to infer a value for the first test by use of the LOS relation than randomly. For the known segment length, the optimal average travel speed can be determined from the desired value. An inquiry may still locate actual traffic volumes, but the scientific evidence for the first trial can conclude that the target amount of traffic. Following this concept Equation (2) can be modified as

CT = [ ( T w , existing − T w , desired ) δ w ω X existing − X desired + ( T m , existing − T m , desired ) δ m ω X existing − X desired ] x (3)

where,

existing = condition at existing LOS; desired = condition at desired LOS. The rest of the parameters remain the same.

Using “Equation (3)” congestion toll for each segment can be estimated where the traffic flow is equal to the desired traffic flow (x = x_{desired}).

The fact that the congest toll calculated with Equation (2) does not guarantee that LOS is exactly as desired, though, may be seen to increase LOS certainly given the facts of other countries. Upon having defined the estimated toll, more optimum congestion toll values can be found from the effect of the pricing. Congestion pricing itself is a form of research and error, and after daily traffic demand and supply, review modifications are taken. As regards the secondary data, the optimum congestion fee can only be given by the adjustment in traffic movement (noted x in Equation (2)), since the key objective of congestion pricing is to demand control. If private car counts do not reduce the congestion levels at the planned amount, then the next change would immediately raise. Likewise, the price would drop if the counts of private cars dip below the allowed amount of congestion.

Primary data is gathered during three working days a week in August 2019 from the field survey for this report. The volume count was calculated at the time of the peak hour (7:30 to 9:30am) from Sunday for a total of four outlets (

The total amount of traffic in the entire segment is 698 vehicles per hour for Segment 1 and 652 vehicles per hour for Segment 2. However, if the metro service is open, these values are not the values of traffic volume. Therefore, it is appropriate to adjust the traffic value of segments 1 and 2. Since the only public transit accessible in those segments today is bus service, the amount of bus traffic from the existing volume of traffic, provided the passengers of these busses are transferred to metro if the service is operating, is deducted from the current volume of traffic. The adjusted volume of segment 1 and segment 2 is according to this definition, respectively 515 veh/hr/ lane and 458 veh/lane.

For travel time data, three private cars (passenger car unit) were used to drive in both segments starting from Mirpur 10 Intersection up to Bijoy Sarani Intersection in three weekdays during peak hours (7:30-9:30AM). The travel time at the moving state for each segment was recorded separately.

The waiting time at Agargaon Intersection and Bijoy Sarani Intersection were also taken all three days which was the red time for the vehicles moving towards Bijoy Sarani from Mirpur 10. The average travel time at moving state for segment 1 is found 33 mins. 9 s and for segment 2 the value is 8 mins. 57 s. The average waiting time at Agargaon Intersection and Bijoy Sarani Intersection are respectively 2 mins. 29 s and 2 mins. 47 s.

Now, it is possible to check the existing vehicle LOS at both segment 1 and segment 2 consisting of 3.7 km and 1.4 respectively using the moving state travel time. By simple arithmetic calculation, the average travel speed at segment 1 is found 4.17 mi/hr and for segment 2 the value is 5.83 mi/hr. For average travel speed of 4.17 mi/hr with calculated based free-flow speed 50 mi/hr and volume to capacity ratio 0.95 consulting

LOS | Travel Speed Threshold by Base Free-Flow Speed (mi/h) | Volume-to-Capacity Ratio | ||||||
---|---|---|---|---|---|---|---|---|

55 | 50 | 45 | 40 | 35 | 30 | 25 | ||

A | >44 | >40 | >36 | >32 | >28 | >24 | >20 | ≤1.0 |

B | >37 | >34 | >30 | >27 | >23 | >20 | >17 | |

C | >28 | >25 | >23 | >20 | >18 | >15 | >13 | |

D | >22 | >20 | >18 | >16 | >14 | >12 | >10 | |

E | >17 | >15 | >14 | >12 | >11 | >9 | >8 | |

F | ≤17 | ≤15 | ≤14 | ≤12 | ≤11 | ≤9 | ≤8 | |

F | Any | >1.0 |

Note:Volume-to-Capacity Ratio of through movement at downstream boundary intersection.

Control Delay (s/veh) | LOS by Volume-to-Capacity Ratio | |
---|---|---|

≤1.0 | >1.0 | |

≤10 | A | F |

>10 - 20 | B | F |

>20 - 35 | C | F |

>35 - 55 | D | F |

>55 - 80 | E | F |

>80 | F | F |

However, the price of congestion can be expected for a higher LOS as the scale of traffic reduces and thus increases the speed and the wait. While congestion costs prevent the usage of private cars, the private vehicles are not on the loser’s side. In reality, in exchange for congestion, LOS is getting stronger. However, it will not be reasonable to predict congestion prices that desire a decent LOS, provided the present traffic conditions. Therefore, the desired LOS is believed to be C for both segments and crossings.

Now consulting

It is difficult to predict how much the traffic volume will reduce due to congestion pricing. However, a certain level of traffic volume can be expected to reduce considering the empirical evidence in other countries. As mentioned in section 2 Singapore’s traffic volume during the non-ALS period and ALS period were found 600 veh/hr/lane and 450 veh/hr/lane where the traffic volume reduced to three-fourth due to congestion pricing. Therefore, the traffic volumes at segment 1 and segment 2 at desired LOS are assumed 386 veh/hr/lane and 344 veh/hr/lane which are three-fourth of their current traffic volume. All moving time and waiting time data, traffic volume data are summarized in

Substituting travel time and waiting time values in the unit of an hour and the traffic volume in the unit of veh/hr/lane in Equation (3) two expressions for two segments are obtained to estimate congestion toll for the private car. (passenger car unit).

CT 1 = [ ( 0.031667 δ w + 0.460556 δ m ) ω 129 ] x

CT 2 = [ ( 0.036667 δ w + 0.115278 δ m ) ω 114 ] x

Data List | Segment 1 | Segment 2 |
---|---|---|

Travel Time at moving state (min) | ||

Existing LOS | 33.15 | 8.95 |

Desired LOS | 5.52 | 2.03 |

Waiting time at Intersections (s) | ||

Existing LOS | 149 | 167 |

Desired LOS | 35 | 35 |

Traffic Volume (veh/hr/lane) | ||

Existing LOS | 515 | 458 |

Desired LOS | 386 | 344 |

Using these expressions congestion toll for any traffic level at the corresponding segment can be estimated.

For the value of ω (average wage rate) the average car owner’s wage rate is more realistic to estimate congestion toll as it is mainly imposed on the private car. But due to the unavailability of any national census data for car owner’s wage 4th-grade wage rate of the 8th national pay scale of Bangladesh is considered as the average wage rate of car owners that is equal to 50,000 BDT/month ($588.24) or 288 BDT/hr ($3.39/hr) [

In a study, Khan & Islam (2013) used a value of 80 BDT/hr ($0.94) as the value of time for the car during peak hours in Dhaka city. This value the value of time is 28% of the average car owner’s wage rate used in this study (80/288 = 0.28). Hence, as a base case, the congestion toll can be estimated for the combination (δ_{w}, δ_{m}) = (0.28, 0.28). But as the drivers value the waiting time at intersections more than the moving time [_{w} = 0.75 quite arbitrarily to capture the fact that δ_{w} is generally greater than δ_{m}. In Bangladesh, there is no record of separate data for the value of waiting time and moving time. So, in this study a pair of (δ_{w}, δ_{m}) = (0.75, 0.28) is considered as the second case to estimate toll because this concept is an important part of the methodology.

Now substituting the values of ω in the unit of BDT/hr and (δ_{w}, δ_{m}) in those expressions’ congestion toll for a private car can be estimated for desired traffic flow which is 386 veh/hr/lane for segment 1 and 344 veh/hr/lane for segment 2. Therefore, in the base case (δ_{w}, δ_{m}) = (0.28, 0.28) congestion toll is estimated 119 BDT ($1.4) and 37 BDT ($0.44) for segment 1 and segment 2 respectively. Congestion toll in the second case (δ_{w}, δ_{m}) = (0.75, 0.28) are 132 BDT ($1.55) for segment 1 and 52 BDT ($0.61) for segment 2. The overall toll charge is between $0.3 - $0.44 per private vehicle. Between these two cases, estimated toll values in the base case are more realistic because there may lie a significant difference in how waiting time is valued in Singapore and Bangladesh as the economic condition of people in these two countries differs a lot. The estimated charge will vary in peak & off-peak hours, weekdays and weekend as the traffic volume fluctuate every time.

This paper develops a congestion price scope in Dhaka City; however, congestion pricing in Bangladesh has not been enforced. The results show affordable road pricing after the modified methodology is implemented where the original method is developed in Singapore. Most notably, the approach needs relatively minimal primary information. It is necessary to change the congestion toll by adjusting the traffic pattern. This study can seem early since the Metro Rail Service is not yet operating; however, there is a lack of data on the price of congestion in the Dhaka Area, such as the prices of time or average car owner’s salary and other difficulties. This analysis incorporates certain conclusions that more reliable values may be derived from. Using revised data in the future using the same approach following the commencement of the metro rail services is necessary. Since, congestion prices have some political aspects, even if the government does not use this congestion price process, it may be enforced temporarily following this study by the government to verify congestion price acceptance level in Bangladesh.

A few more considerations, such as public acceptance, need to be discussed in depth before congestion pricing is implemented. An evaluation will help to determine whether the price is satisfactory or not before and during the trial. The automated toll scheme can also be used to reduce the vehicle queue at the segment entry stage. When the car moves through the gantry, it will retrieve money through DSRC or infrared technology from the transponder fitted to a dashboard, and the amount will be pre-loaded inside it. Before launching the service, the Gantry location should be set. The category of automobile that is exempted by the price scheme is necessary to repair. Emergency cars, such as ambulances, fire, police, municipal buses, minibuses, other vehicles such as freight transport and bicycles, are excluded from the toll scheme in the implemented cities (FHWA, 2008).

It can be hoped that in the future the government will introduce a Pricing Division in Dhaka town not only to minimize congestion but also optimize riding in MRT. In this study, only passenger cars and through movement traffic were considered for congestion pricing. Also, a few more limitations should be addressed such as traffic amount after MRT-6 and wage amount. Further study is necessary to come up with a concrete pricing amount. Future studies should include road pricing based on different vehicle types, area type, traffic operation timing, as well as impact of the prices on users, travel behavior, the impact of air quality, space mean speed, and trip number on pricing implemented area.

The authors are grateful to Tanay Datta Chowdhury, Muntahith Mehadil Orvin and Md. Anwar Uddin for their assistance.

The authors declare no conflicts of interest regarding the publication of this paper.

Islam, M.M., Saha, N., Rahman, Md.S. and Ahmed, N.U. (2021) Congestion Pricing of Urban Street Segments along with MRT Route—The Case Study of Dhaka, Bangladesh. Journal of Transportation Technologies, 11, 168-178. https://doi.org/10.4236/jtts.2021.112011